Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks
This addresses performance degradation in multi-task learning for AI practitioners, offering an incremental improvement over task-level weighting methods.
The paper tackles harmful interference in multi-task learning by proposing SLGrad, a sample-level weighting algorithm that reshapes task distributions to eliminate harmful signals and augment useful ones, resulting in substantial generalization performance gains on synthetic and supervised datasets.
Multi-task learning (MTL) can improve the generalization performance of neural networks by sharing representations with related tasks. Nonetheless, MTL can also degrade performance through harmful interference between tasks. Recent work has pursued task-specific loss weighting as a solution for this interference. However, existing algorithms treat tasks as atomic, lacking the ability to explicitly separate harmful and helpful signals beyond the task level. To this end, we propose SLGrad, a sample-level weighting algorithm for multi-task learning with auxiliary tasks. Through sample-specific task weights, SLGrad reshapes the task distributions during training to eliminate harmful auxiliary signals and augment useful task signals. Substantial generalization performance gains are observed on (semi-) synthetic datasets and common supervised multi-task problems.